5 Differences between Machine Learning and Artificial Intelligence (AI)

Technological developments have a big impact on every company. Therefore, it is not surprising that now the majority of companies from various business niches and scales have placed data processing and data analytics as an important part of business processes.


This is why understanding machine learning and artificial intelligence (AI) is something that you must understand. Both are very popular terms in the world of technology. Especially in the midst of the rise of marketing technology in the digital marketing era like now.

Because in the era of business digitalization or digital transformation as it is today, the amount of data companies have will increase on a large scale (big data). Thus, companies need a computational process to analyze the big data in order to produce useful information.

Unfortunately, not infrequently there are still business people who do not understand what the difference between the two is. Many consider that both play a role in facilitating the computational process through algorithms.

In fact, the combination of AI and machine learning can actually produce business process models that can analyze data accurately. So, what’s the difference between the two?

Machine Learning and Artificial Intelligence

Basically, currently there are lots of new terms that have sprung up along with the need for data analysis. AI and Machine Learning are no exception.

Both of them play an important role in the process of data management and data analytics which are important pillars for business growth in the marketing 5.0 era as it is today.

For more details, see the following review of the definitions of machine learning and AI.

What is Machine Learning?

Launching from Forbes, machine learning is a scientific field regarding computer algorithms that are useful for automatically improving the performance of computer programs based on data.

The way it works is to collect, process, and compare data (from small to large) to look for patterns and analyze the differences. An example is when you create a model to detect apple images, the output will only provide results for apple images. So, if you provide new data in the form of an orange image, then the results are irrelevant.

There are three types of machine learning that you should know about, namely supervised learning, unsupervised learning, and reinforcement learning.

  • Supervised learning : An algorithm that is useful for predicting the output value of new data based on relationships and patterns from previous data.
  • Unsupervised learning : Algorithm that functions to detect patterns and descriptive modeling. Unsupervised learning does not have output categories on data (such as training data and test data).
  • Reinforcement learning : An algorithm that is useful for maximizing output and minimizing risk. Machine learning itself is an important element in every data processing process. Starting from data ingestion, data mining, data mapping, data science, and so on.

Currently, companies often use machine learning for online product recommendation systems, Google search algorithms, e-mail spam filters, targeted advertising, retargeting for ad campaigns and ad strategies, suggestions on social media, and much more.


What Is Artificial Intelligence?

After knowing the definition of machine learning, now we will review what AI is.

The term Artificial Intelligence itself is now very familiar to the ear. In fact, the majority of people have unknowingly used AI in their daily lives. Starting from the use of smartphones, smart TVs, Google Home, Siri, and other technologies.

So, we can say that Artificial Intelligence is a field of computer science that is useful for creating intelligent machines that can work like humans. The term AI itself has appeared since 1956, and continues to experience fluctuating developments to this day.

AI is divided into two types. Namely Narrow AI and Artificial General Intelligence.

  • Narrow AI : Is a simulation of human intelligence. This type of AI focuses on performing one task accurately through the use of machines, but still works under human intelligence. Examples are Google search, Siri, Image recognition software, etc.
  • Artificial General Intelligence (AGI): Is a type of AI that is programmed the same as human intelligence. So that it is the same as humans, this type of AI can also solve any problem. We can also call AGI the term intelligent assistant robot.

Meanwhile, the application of AI in everyday life, especially in the business world, has often been encountered in various forms. Some examples are chatbots that can provide automatic answers, Generative AI which is useful for digital marketing technology, to Immersive Buy Journey to provide a more effective user experience for customers.

AI itself is the forerunner of today’s intelligent machine development by imitating human abilities. Including machine learning and deep learning which are sub-fields of AI.

Nevertheless, AI is not intended to replace the role of humans. This is the main concern in society 5.0 and marketing 5.0 today, where the use of AI is to help human work, rather than replace it.


Difference between Machine Learning and Artificial Intelligence

Difference between Machine Learning and Artificial Intelligence

After knowing the meaning of machine learning and AI, then what is the difference between the two?

We need to know that basically machine learning is part of artificial intelligence.

AI works to increase the chances of success of a system or machine. Whereas machine learning tends to focus more on the accuracy of the system or the machine itself.

Or in other words, AI is useful for solving a particular problem, for example decision making, through a system that mimics humans. Meanwhile, machine learning is useful for studying patterns and relationships from existing data to maximize the performance of a system/machine and help algorithms work automatically.

For more details, here are the differences between machine learning and AI:

Output: AI produces output in the form of knowledge or knowledge. Meanwhile, machine learning produces output in the form of data.

Purpose : AI aims to develop a system capable of emulating human intelligence to solve problems. Meanwhile, machine learning aims to develop algorithms that can learn independently from existing data.

Function : The main function of AI is to create intelligent systems to perform human-like tasks. Meanwhile, machine learning functions to teach machines to do according to data, so as to provide accurate results.

Data Type: In general, AI uses unstructured, semi-structured, to structured data types and types. Meanwhile, machine learning only uses structured and semi-structured data.

Example of Implementation: Implementation of AI, including smart assistants, chatbots, expert systems, etc. Meanwhile, the implementation of machine learning includes recommendation engines (for example recommendations for movies on streaming services and products on e-commerce based on consumer behavior), Google search algorithms, recommendations for Facebook photo tagging, and so on.


Based on the reviews above, we can conclude that the fundamental difference between the two is that machine learning is part of artificial intelligence itself. However, both AI and machine learning both play an important role in the current era of technological development.

Thus, understanding both is important to maximize business processes and business continuity. Therefore, it is not surprising that an understanding of AI and machine learning is a must-have competency for data analysts and data scientists.